Education Technologies

In this thesis, the goal is to enhance the contextual awareness of a GPT-based educational chatbot, named IRIS, on the Artemis Learning Platform by incorporating lecture content. To do this, the lecture slides should be embedded into a vector database, and the chatbot should be able to retrieve the most relevant slides based on the user’s query in order to provide the most relevant answer.

The challenges encountered by students during the completion of exercises necessitate the implementation of a proactive assistance mechanism within Artemis. This could potentially be achieved through the integration of generative AI technologies such as ChatGPT. The objective of this thesis is to augment Artemis with the capability to provide automatic and proactive assistance to students when they encounter difficulties. The effectiveness and impact of this approach on the learning experience will be evaluated through a comprehensive assessment.

The thesis aims to enhance the instructor interface and student experience in the IRIS learning platform. The project will introduce a thumbnail grid for instructors to manage slides intuitively, ensuring persistent configurations even when slides are updated. The system will adapt to changes, such as the addition of new slides, by automatically adjusting the selection and maintaining the overall structure. Advanced features like automatic slide number extraction and adaptive reference management will be implemented to maintain continuity when slides are updated. The project also aims to improve the student experience by enabling direct browser viewing of lecture slides, moving away from traditional PDF downloads. This approach will provide a more seamless and integrated learning experience, with functionalities like slide hiding synchronized with exercise deadlines. The in-browser viewing experience will be interactive, allowing students to engage with the material dynamically. Additional features to enhance user experience include interactive annotations, real-time communication integration on lecture slides, and a dark mode option for PDF viewing in the browser. This project will significantly improve the usability and functionality of the IRIS learning platform, fostering a more structured and paced learning environment.

The goal of this thesis is to evaluate the effectiveness of IRIS, a GPT-based chatbot for higher education. The chatbot is integrated into the Artemis learning platform and is designed to provide assistance to students when they encounter difficulties. In addition, the chatbot is capable of answering questions related to the course content. Instructors can benefit from IRIS through assistance in exercise generation.

In this thesis, the effectiveness of IRIS will be evaluated through a combination of quantitative and qualitative methods. The quantitative evaluation will be conducted through a comprehensive assessment of the chatbot’s performance both in real course and experimental settings. The qualitative evaluation will be conducted through a survey of students and instructors and expert interviews. The results of the evaluation will be used to identify the strengths and weaknesses of the chatbot and to provide recommendations for future improvements.

Competency-based education(CBE) enables flexible, skill-centered learning by allowing students to progress based on mastery rather than fixed schedules. Atlas already utilizes machine learning techniques to support educators; nevertheless, further improvements are required to accelerate adoption and improve quality.

This thesis proposes an interactive AI-powered agent that assists instructors in creating, refining, and maintaining competency networks through natural language. Rather than relying on static, one-shot inputs, the agent engages in a dynamic conversation—asking clarifying questions, gathering relevant context, and presenting interactive proposals. This interaction model is designed to reduce redundant AI calls, avoid naive prompting, and ensure that the resulting competency networks align closely with course objectives.

The increasing importance of computational literacy drives efforts to introduce computer science concepts early in education. However, many primary school teachers lack formal technical training, making selecting engaging, pedagogically sound activities difficult. Existing tools often lack personalization, leading to ineffective choices, while disparities in technology access highlight the need to integrate unplugged, digital, and hybrid approaches.

This thesis proposes a recommendation system to help teachers select suitable computer science activities based on age group, format, resources, and educational objectives. The system will leverage a comprehensive tagging framework grounded in pedagogical principles and compare different recommendation approaches for accuracy and scalability. This research aims to empower teachers with a personalized tool that boosts confidence in teaching computer science while enhancing student motivation and creating more inclusive learning experiences.

Doctoral application portals manage admissions and shape candidate experience. Many systems create barriers through complex interfaces and inconsistent design. TUMApply at the Technical University of Munich replaces an earlier system but lacks a clear, inclusive, and consistent interface that supports efficient task completion and equitable participation.

This thesis applies Nielsen’s usability heuristics with gender-sensitive and accessible design practices to identify and address usability barriers and inclusivity weaknesses. The primary contribution is a redesigned interface that demonstrates how doctoral application portals can better serve diverse user needs while maintaining efficiency. This work offers practical insights for building academic platforms that support usability and inclusivity, grounded in systematic analysis, iterative design, and implementation.

This thesis develops an analog story-coding activity using Ozobot for German elementary school education. The activity combines storytelling with hands-on robotics to introduce fundamental programming concepts and computational thinking skills. Pupils guide the robot through stories using color-coded cards, without the need for text- or codeblock-based programming. Through iterative testing, the thesis examines the impact of this approach on pupils’ motivation, the activity’s usability and it’s feasibility in real teaching conditions. The thesis summarizes the findings to provide recommendations for designing story-driven analog robotics activities for elementary schools.

Competency-aware educational recommender systems enable personalized learning, but the absence of standardized benchmark datasets prevents systematic algorithmic comparison and limits progress. This thesis develops a collaborative platform to standardize the collection of ground-truth competency networks across institutions. Contributors map competency relations and link them to learning resources, producing an open benchmark dataset for the performance evaluation of recommenders. The initial release focuses on computer science to seed the dataset and integrates guided onboarding and light gamification to support sustained contributions.

This Master’s thesis presents the design and implementation of a digital story-coding activity for elementary education using Ozobots. It combines narrative elements with block-based programming to introduce core computational thinking concepts in an engaging and accessible way. The study develops a customized Ozoblockly environment in German, reducing technical barriers and preparation effort for teachers. The story-driven approach makes programming tangible and expressive, fostering creativity, motivation, and inclusive participation. User testing in classrooms examines pupils’ engagement, intuitive use of programming concepts, and classroom feasibility, providing formative insights to guide iterative improvements. The results inform the design of low-threshold, inclusive computational thinking materials for early education.

This bachelor’s thesis aims to advance the Ozobot Board Game by transforming an existing prototype of an educational board game into a sustainable and classroom-ready product. The project focuses on developing a durable, engaging, and easy-to-use tool for teaching basic programming concepts. It seeks to enhance the game’s replayability while promoting equal participation and interest in computing education among all children, regardless of gender or cultural background. The design includes differentiated learning tasks, dynamic difficulty adjustment, and iterative refinement through user feedback and classroom testing. The final outcome will be a classroom-ready board game package, including adaptable learning resources and a teacher’s manual, designed for inclusive and flexible use in diverse educational settings.

Competency-based educational recommender systems enable personalized learning, but the absence of standardized benchmark datasets prevents systematic algorithmic comparison. This thesis develops a platform for collaborative competency relationship data collection with four objectives: a generalized database model, authentication systems for credential verification, administrative interfaces for dataset management and export, and production deployment infrastructure. The system enables systematic collection of high-quality competency mapping data for creating reliable benchmarks.